On Periodic Scheduling of Bandwidth Reservations with Deadline Constraint for Big Data Transfer

Yongqiang Wang, Chase Q. Wu, Aiqin Hou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

The efficiency of bandwidth scheduling in high-performance networks is critical to the utilization of network resources and the satisfaction of user requests. In this paper, we formulate a periodic bandwidth scheduling problem to maximize the number of satisfied user requests for bandwidth reservation with deadline constraint on a fixed network path, referred to as multiple deadline-constrained bandwidth scheduling (M-DCBS). We show the NP-completeness of this problem and propose a Maximum User Number Resource Reservation Algorithm (MUNRRA). Extensive simulation results show that MUNRRA exhibits a superior performance over existing algorithms in terms of scheduling success ratio and execution time. Considering the popularity of the DCBS-based service model and the rapid expansion of high-performance networks in both speed and scope, the proposed scheduling algorithm has great potential to improve the network performance of big data applications that require the DCBS service for data transfer.

Original languageEnglish (US)
Title of host publicationProceedings - 2016 IEEE 41st Conference on Local Computer Networks, LCN 2016
PublisherIEEE Computer Society
Pages224-227
Number of pages4
ISBN (Electronic)9781509020546
DOIs
StatePublished - Dec 22 2016
Event41st IEEE Conference on Local Computer Networks, LCN 2016 - Dubai, United Arab Emirates
Duration: Nov 7 2016Nov 10 2016

Publication series

NameProceedings - Conference on Local Computer Networks, LCN

Other

Other41st IEEE Conference on Local Computer Networks, LCN 2016
Country/TerritoryUnited Arab Emirates
CityDubai
Period11/7/1611/10/16

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture

Keywords

  • High-performance networks
  • bandwidth scheduling
  • big data
  • resource utilization

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